Discovery of middleboxes using traffic flow stitching

ABSTRACT

Systems, methods, and computer-readable media for flow stitching network traffic flow segments across middleboxes. A method can include collecting flow records of traffic flow segments at a first middlebox and a second middlebox in a network environment including one or more transaction identifiers assigned to the traffic flow segments. Sources and destinations of the traffic flow segments can be identified with respect to the first middlebox and the second middlebox. Corresponding subsets of the traffic flow segments can be stitched together to from a first stitched traffic flow at the first middlebox and a second stitched traffic flow at the second middlebox. The first and second stitched traffic flows can be stitched together to form a cross-middlebox stitched traffic flow across the first middlebox and the second middlebox. The cross-middlebox stitched traffic flow can be incorporated as part of network traffic data for the network environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/622,008, filed on Jan. 25, 2018, entitled “Discovery of MiddleBoxes Using Traffic Flow Stitching,” the content of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present technology pertains to network traffic flow stitching andflow stitching network traffic flow segments across middleboxes in anetwork environment.

BACKGROUND

Currently, sensors deployed in a network can be used to gather networktraffic data related to nodes operating in the network. The networktraffic data can include metadata relating to a packet, a collection ofpackets, a flow, a bidirectional flow, a group of flows, a session, or anetwork communication of another granularity. That is, the networktraffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

Gathered network traffic data can be analyzed to provide insights intothe operation of the nodes in the network, otherwise referred to asanalytics. In particular, discovered application or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, and network flows can be determined for the network using thenetwork traffic data.

Sensors deployed in a network can be used to gather network traffic dataon a client and server level of granularity. For example, networktraffic data can be gathered for determining which clients arecommunicating which servers and vice versa. However, sensors are notcurrently deployed or integrated with systems to gather network trafficdata for different segments of traffic flows forming the traffic flowsbetween a server and a client. Specifically, current sensors gathernetwork traffic data as traffic flows directly between a client and aserver while ignoring which nodes, e.g. middleboxes, the traffic flowsactually pass through in passing between a server and a client. Morespecifically, current sensors are not configured to gather networktraffic data as traffic flows pass through multiple middleboxes betweena server and a client. This effectively treats the network environmentbetween servers and clients as a black box and leads to gaps in networktraffic data and traffic flows indicated by the network traffic data.

In turn, such gaps in network traffic data and corresponding trafficflows can lead to deficiencies in diagnosing problems within a networkenvironment. For example, a problem stemming from an incorrectlyconfigured middlebox might be diagnosed as occurring at a client as theflow between the client and a server is treated as a black box eventhough it actually passes through one or more middleboxes. In anotherexample, gaps in network traffic data between a server and a client canlead to an inability to determine whether policies are correctlyenforced at middleboxes between the server and the client. Theretherefore exist needs for systems, methods, and computer-readable mediafor generating network traffic data at nodes between servers andclients, e.g. at multiple middleboxes in a chain of middleboxes betweenthe servers and clients. In particular, there exist needs for systems,methods, and computer-readable media for stitching together trafficflows that pass through multiple nodes between servers and clients togenerate more complete and detailed traffic flows, e.g. between theservers and the clients.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example network traffic monitoring system;

FIG. 2 illustrates an example of a network environment;

FIG. 3 depicts a diagram of an example network environment for stitchingtogether traffic flow segments across multiple nodes between a clientand a server;

FIG. 4 illustrates a flowchart for an example method of forming across-middlebox stitched traffic flow across middleboxes;

FIG. 5 shows an example middlebox traffic flow stitching system;

FIG. 6 illustrates an example network device in accordance with variousembodiments; and

FIG. 7 illustrates an example computing device in accordance withvarious embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationscan be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which can beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms can be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles can be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

A method can include collecting flow records of traffic flow segments atboth a first middlebox and a second middlebox in a network environmentcorresponding to one or more traffic flows passing through either orboth the first middlebox and the second middlebox. The flow records caninclude one or more transaction identifiers assigned to the trafficflows. Sources and destinations of the traffic flow segments in thenetwork environment with respect to either or both the first middleboxand the second middlebox can be identified using the flow records. Themethod can include stitching together a subset of the traffic flowsegments to form a first stitched traffic flow at the first middlebox inthe network environment based on the one or more transaction identifiersassigned to the traffic flow segments and the sources and destination ofthe traffic flow segments in the network environment with respect to thefirst middlebox. Additionally, the method can include stitching togetheranother subset of the traffic flow segments to form a second stitchedtraffic flow at the second middlebox in the network environment based onthe one or more transaction identifiers assigned to the traffic flowsegments and the sources and destination of the traffic flow segments inthe network environment with respect to the second middlebox. The firststitched traffic flow and the second stitched traffic flow can bestitched together to form a cross-middlebox stitched traffic flow acrossthe first middlebox and the second middlebox. Subsequently, thecross-middlebox stitched traffic flow can be incorporated as part ofnetwork traffic data for the network environment.

A system can collect flow records of traffic flow segments at both afirst middlebox and a second middlebox in a network environmentcorresponding to one or more traffic flows passing through either orboth the first middlebox and the second middlebox. The flow records caninclude one or more transaction identifiers assigned to the trafficflows. The system can identify sources and destinations of the trafficflow segments in the network environment with respect to either or boththe first middlebox and the second middlebox using the flow records. Asubset of the traffic flow segments can be stitched together to form afirst stitched traffic flow at the first middlebox in the networkenvironment based on the one or more transaction identifiers assigned tothe traffic flow segments and the sources and destination of the trafficflow segments in the network environment with respect to the firstmiddlebox. Additionally, the system can stitch together another subsetof the traffic flow segments to form a second stitched traffic flow atthe second middlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and thesources and destination of the traffic flow segments in the networkenvironment with respect to the second middlebox. The first stitchedtraffic flow and the second stitched traffic flow can be stitchedtogether to form a cross-middlebox stitched traffic flow across thefirst middlebox and the second middlebox.

A system can collect flow records of traffic flow segments at both afirst middlebox and a second middlebox in a network environmentcorresponding to one or more traffic flows passing through either orboth the first middlebox and the second middlebox. The flow records caninclude one or more transaction identifiers assigned to the trafficflows. The system can identify sources and destinations of the trafficflow segments in the network environment with respect to either or boththe first middlebox and the second middlebox using the flow records. Asubset of the traffic flow segments can be stitched together to form afirst stitched traffic flow at the first middlebox in the networkenvironment based on the one or more transaction identifiers assigned tothe traffic flow segments and the sources and destination of the trafficflow segments in the network environment with respect to the firstmiddlebox. Additionally, the system can stitch together another subsetof the traffic flow segments, including common traffic flow segmentswith the subset of the traffic flow segments, to form a second stitchedtraffic flow at the second middlebox in the network environment based onthe one or more transaction identifiers assigned to the traffic flowsegments and the sources and destination of the traffic flow segments inthe network environment with respect to the second middlebox. The firststitched traffic flow and the second stitched traffic flow can bestitched together to form a cross-middlebox stitched traffic flow acrossthe first middlebox and the second middlebox.

Example Embodiments

The disclosed technology addresses the need in the art for monitoringnetwork environments, e.g. to diagnose and prevent problems in thenetwork environment. The present technology involves system, methods,and computer-readable media for stitching together traffic flows acrossnodes between servers and clients to provide more detailed networktraffic data, e.g. for diagnosing and preventing problems in a networkenvironment.

The present technology will be described in the following disclosure asfollows. The discussion begins with an introductory discussion ofnetwork traffic data collection and a description of an example networktraffic monitoring system and an example network environment, as shownin FIGS. 1 and 2. A discussion of example systems and methods forstitching together stitched network traffic flows across nodes, as shownin FIGS. 3-5, will then follow. A discussion of example network devicesand computing devices, as illustrated in FIGS. 6 and 7, will thenfollow. The disclosure now turns to an introductory discussion ofnetwork sensor data collection based on network traffic flows andclustering of nodes in a network for purposes of collecting data basedon network traffic flows.

Sensors implemented in networks are traditionally limited to collectingpacket data at networking devices. In some embodiments, networks can beconfigured with sensors at multiple points, including on networkingdevices (e.g., switches, routers, gateways, firewalls, deep packetinspectors, traffic monitors, load balancers, etc.), physical servers,hypervisors or shared kernels, virtual partitions (e.g., VMs orcontainers), and other network elements. This can provide a morecomprehensive view of the network. Further, network traffic data (e.g.,flows) can be associated with, or otherwise include, host and/orendpoint data (e.g., host/endpoint name, operating system, CPU usage,network usage, disk space, logged users, scheduled jobs, open files,information regarding files stored on a host/endpoint, etc.), processdata (e.g., process name, ID, parent process ID, path, CPU utilization,memory utilization, etc.), user data (e.g., user name, ID, login time,etc.), and other collectible data to provide more insight into networkactivity.

Sensors implemented in a network at multiple points can be used tocollect data for nodes grouped together into a cluster. Nodes can beclustered together, or otherwise a cluster of nodes can be identifiedusing one or a combination of applicable network operation factors. Forexample, endpoints performing similar workloads, communicating with asimilar set of endpoints or networking devices, having similar networkand security limitations (i.e., policies), and sharing other attributescan be clustered together.

In some embodiments, a cluster can be determined based on early fusionin which feature vectors of each node comprise the union of individualfeature vectors across multiple domains. For example, a feature vectorcan include a packet header-based feature (e.g., destination networkaddress for a flow, port, etc.) concatenated to an aggregate flow-basedfeature (e.g., the number of packets in the flow, the number of bytes inthe flow, etc.). A cluster can then be defined as a set of nodes whoserespective concatenated feature vectors are determined to exceedspecified similarity thresholds (or fall below specified distancethresholds).

In some embodiments, a cluster can be defined based on late fusion inwhich each node can be represented as multiple feature vectors ofdifferent data types or domains. In such systems, a cluster can be a setof nodes whose similarity (and/or distance measures) across differentdomains, satisfy specified similarity (and/or distance) conditions foreach domain. For example, a first node can be defined by a first networkinformation-based feature vector and a first process-based featurevector while a second node can be defined by a second networkinformation-based feature vector and a second process-based featurevector. The nodes can be determined to form a cluster if theircorresponding network-based feature vectors are similar to a specifieddegree and their corresponding process-based feature vectors are only aspecified distance apart.

Referring now to the drawings, FIG. 1 is an illustration of a networktraffic monitoring system 100 in accordance with an embodiment. Thenetwork traffic monitoring system 100 can include a configurationmanager 102, sensors 104, a collector module 106, a data mover module108, an analytics engine 110, and a presentation module 112. In FIG. 1,the analytics engine 110 is also shown in communication with out-of-banddata sources 114, third party data sources 116, and a network controller118.

The configuration manager 102 can be used to provision and maintain thesensors 104, including installing sensor software or firmware in variousnodes of a network, configuring the sensors 104, updating the sensorsoftware or firmware, among other sensor management tasks. For example,the sensors 104 can be implemented as virtual partition images (e.g.,virtual machine (VM) images or container images), and the configurationmanager 102 can distribute the images to host machines. In general, avirtual partition can be an instance of a VM, container, sandbox, orother isolated software environment. The software environment caninclude an operating system and application software. For softwarerunning within a virtual partition, the virtual partition can appear tobe, for example, one of many servers or one of many operating systemsexecuted on a single physical server. The configuration manager 102 caninstantiate a new virtual partition or migrate an existing partition toa different physical server. The configuration manager 102 can also beused to configure the new or migrated sensor.

The configuration manager 102 can monitor the health of the sensors 104.For example, the configuration manager 102 can request for statusupdates and/or receive heartbeat messages, initiate performance tests,generate health checks, and perform other health monitoring tasks. Insome embodiments, the configuration manager 102 can also authenticatethe sensors 104. For instance, the sensors 104 can be assigned a uniqueidentifier, such as by using a one-way hash function of a sensor's basicinput/out system (BIOS) universally unique identifier (UUID) and asecret key stored by the configuration image manager 102. The UUID canbe a large number that can be difficult for a malicious sensor or otherdevice or component to guess. In some embodiments, the configurationmanager 102 can keep the sensors 104 up to date by installing the latestversions of sensor software and/or applying patches. The configurationmanager 102 can obtain these updates automatically from a local sourceor the Internet.

The sensors 104 can reside on various nodes of a network, such as avirtual partition (e.g., VM or container) 120; a hypervisor or sharedkernel managing one or more virtual partitions and/or physical servers122, an application-specific integrated circuit (ASIC) 124 of a switch,router, gateway, or other networking device, or a packet capture (pcap)126 appliance (e.g., a standalone packet monitor, a device connected toa network devices monitoring port, a device connected in series along amain trunk of a datacenter, or similar device), or other element of anetwork. The sensors 104 can monitor network traffic between nodes, andsend network traffic data and corresponding data (e.g., host data,process data, user data, etc.) to the collectors 106 for storage. Forexample, the sensors 104 can sniff packets being sent over its hosts'physical or virtual network interface card (NIC), or individualprocesses can be configured to report network traffic and correspondingdata to the sensors 104. Incorporating the sensors 104 on multiple nodesand within multiple partitions of some nodes of the network can providefor robust capture of network traffic and corresponding data from eachhop of data transmission. In some embodiments, each node of the network(e.g., VM, container, or other virtual partition 120, hypervisor, sharedkernel, or physical server 122, ASIC 124, pcap 126, etc.) includes arespective sensor 104. However, it should be understood that varioussoftware and hardware configurations can be used to implement the sensornetwork 104.

As the sensors 104 capture communications and corresponding data, theycan continuously send network traffic data to the collectors 106. Thenetwork traffic data can include metadata relating to a packet, acollection of packets, a flow, a bidirectional flow, a group of flows, asession, or a network communication of another granularity. That is, thenetwork traffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

The sensors 104 can also determine additional data, included as part ofgathered network traffic data, for each session, bidirectional flow,flow, packet, or other more granular or less granular networkcommunication. The additional data can include host and/or endpointinformation, virtual partition information, sensor information, processinformation, user information, tenant information, applicationinformation, network topology, application dependency mapping, clusterinformation, or other information corresponding to each flow.

In some embodiments, the sensors 104 can perform some preprocessing ofthe network traffic and corresponding data before sending the data tothe collectors 106. For example, the sensors 104 can remove extraneousor duplicative data or they can create summaries of the data (e.g.,latency, number of packets per flow, number of bytes per flow, number offlows, etc.). In some embodiments, the sensors 104 can be configured toonly capture certain types of network information and disregard therest. In some embodiments, the sensors 104 can be configured to captureonly a representative sample of packets (e.g., every 1,000th packet orother suitable sample rate) and corresponding data.

Since the sensors 104 can be located throughout the network, networktraffic and corresponding data can be collected from multiple vantagepoints or multiple perspectives in the network to provide a morecomprehensive view of network behavior. The capture of network trafficand corresponding data from multiple perspectives rather than just at asingle sensor located in the data path or in communication with acomponent in the data path, allows the data to be correlated from thevarious data sources, which can be used as additional data points by theanalytics engine 110. Further, collecting network traffic andcorresponding data from multiple points of view ensures more accuratedata is captured. For example, a conventional sensor network can belimited to sensors running on external-facing network devices (e.g.,routers, switches, network appliances, etc.) such that east-westtraffic, including VM-to-VM or container-to-container traffic on a samehost, may not be monitored. In addition, packets that are dropped beforetraversing a network device or packets containing errors cannot beaccurately monitored by the conventional sensor network. The sensornetwork 104 of various embodiments substantially mitigates or eliminatesthese issues altogether by locating sensors at multiple points ofpotential failure. Moreover, the network traffic monitoring system 100can verify multiple instances of data for a flow (e.g., source endpointflow data, network device flow data, and endpoint flow data) against oneanother.

In some embodiments, the network traffic monitoring system 100 canassess a degree of accuracy of flow data sets from multiple sensors andutilize a flow data set from a single sensor determined to be the mostaccurate and/or complete. The degree of accuracy can be based on factorssuch as network topology (e.g., a sensor closer to the source can bemore likely to be more accurate than a sensor closer to thedestination), a state of a sensor or a node hosting the sensor (e.g., acompromised sensor/node can have less accurate flow data than anuncompromised sensor/node), or flow data volume (e.g., a sensorcapturing a greater number of packets for a flow can be more accuratethan a sensor capturing a smaller number of packets).

In some embodiments, the network traffic monitoring system 100 canassemble the most accurate flow data set and corresponding data frommultiple sensors. For instance, a first sensor along a data path cancapture data for a first packet of a flow but can be missing data for asecond packet of the flow while the situation is reversed for a secondsensor along the data path. The network traffic monitoring system 100can assemble data for the flow from the first packet captured by thefirst sensor and the second packet captured by the second sensor.

As discussed, the sensors 104 can send network traffic and correspondingdata to the collectors 106. In some embodiments, each sensor can beassigned to a primary collector and a secondary collector as part of ahigh availability scheme. If the primary collector fails orcommunications between the sensor and the primary collector are nototherwise possible, a sensor can send its network traffic andcorresponding data to the secondary collector. In other embodiments, thesensors 104 are not assigned specific collectors but the network trafficmonitoring system 100 can determine an optimal collector for receivingthe network traffic and corresponding data through a discovery process.In such embodiments, a sensor can change where it sends it networktraffic and corresponding data if its environments changes, such as if adefault collector fails or if the sensor is migrated to a new locationand it would be optimal for the sensor to send its data to a differentcollector. For example, it can be preferable for the sensor to send itsnetwork traffic and corresponding data on a particular path and/or to aparticular collector based on latency, shortest path, monetary cost(e.g., using private resources versus a public resources provided by apublic cloud provider), error rate, or some combination of thesefactors. In other embodiments, a sensor can send different types ofnetwork traffic and corresponding data to different collectors. Forexample, the sensor can send first network traffic and correspondingdata related to one type of process to one collector and second networktraffic and corresponding data related to another type of process toanother collector.

The collectors 106 can be any type of storage medium that can serve as arepository for the network traffic and corresponding data captured bythe sensors 104. In some embodiments, data storage for the collectors106 is located in an in-memory database, such as dashDB from IBM®,although it should be appreciated that the data storage for thecollectors 106 can be any software and/or hardware capable of providingrapid random access speeds typically used for analytics software. Invarious embodiments, the collectors 106 can utilize solid state drives,disk drives, magnetic tape drives, or a combination of the foregoingaccording to cost, responsiveness, and size requirements. Further, thecollectors 106 can utilize various database structures such as anormalized relational database or a NoSQL database, among others.

In some embodiments, the collectors 106 can only serve as networkstorage for the network traffic monitoring system 100. In suchembodiments, the network traffic monitoring system 100 can include adata mover module 108 for retrieving data from the collectors 106 andmaking the data available to network clients, such as the components ofthe analytics engine 110. In effect, the data mover module 108 can serveas a gateway for presenting network-attached storage to the networkclients. In other embodiments, the collectors 106 can perform additionalfunctions, such as organizing, summarizing, and preprocessing data. Forexample, the collectors 106 can tabulate how often packets of certainsizes or types are transmitted from different nodes of the network. Thecollectors 106 can also characterize the traffic flows going to and fromvarious nodes. In some embodiments, the collectors 106 can match packetsbased on sequence numbers, thus identifying traffic flows and connectionlinks. As it can be inefficient to retain all data indefinitely incertain circumstances, in some embodiments, the collectors 106 canperiodically replace detailed network traffic data with consolidatedsummaries. In this manner, the collectors 106 can retain a completedataset describing one period (e.g., the past minute or other suitableperiod of time), with a smaller dataset of another period (e.g., theprevious 2-10 minutes or other suitable period of time), andprogressively consolidate network traffic and corresponding data ofother periods of time (e.g., day, week, month, year, etc.). In someembodiments, network traffic and corresponding data for a set of flowsidentified as normal or routine can be winnowed at an earlier period oftime while a more complete data set can be retained for a lengthierperiod of time for another set of flows identified as anomalous or as anattack.

The analytics engine 110 can generate analytics using data collected bythe sensors 104. Analytics generated by the analytics engine 110 caninclude applicable analytics of nodes or a cluster of nodes operating ina network. For example, analytics generated by the analytics engine 110can include one or a combination of information related to flows of datathrough nodes, detected attacks on a network or nodes of a network,applications at nodes or distributed across the nodes, applicationdependency mappings for applications at nodes, policies implemented atnodes, and actual policies enforced at nodes.

Computer networks can be exposed to a variety of different attacks thatexpose vulnerabilities of computer systems in order to compromise theirsecurity. Some network traffic can be associated with malicious programsor devices. The analytics engine 110 can be provided with examples ofnetwork states corresponding to an attack and network statescorresponding to normal operation. The analytics engine 110 can thenanalyze network traffic and corresponding data to recognize when thenetwork is under attack. In some embodiments, the network can operatewithin a trusted environment for a period of time so that the analyticsengine 110 can establish a baseline of normal operation. Since malwareis constantly evolving and changing, machine learning can be used todynamically update models for identifying malicious traffic patterns.

In some embodiments, the analytics engine 110 can be used to identifyobservations which differ from other examples in a dataset. For example,if a training set of example data with known outlier labels exists,supervised anomaly detection techniques can be used. Supervised anomalydetection techniques utilize data sets that have been labeled as normaland abnormal and train a classifier. In a case in which it is unknownwhether examples in the training data are outliers, unsupervised anomalytechniques can be used. Unsupervised anomaly detection techniques can beused to detect anomalies in an unlabeled test data set under theassumption that the majority of instances in the data set are normal bylooking for instances that seem to fit to the remainder of the data set.

The analytics engine 110 can include a data lake 130, an applicationdependency mapping (ADM) module 140, and elastic processing engines 150.The data lake 130 is a large-scale storage repository that providesmassive storage for various types of data, enormous processing power,and the ability to handle nearly limitless concurrent tasks or jobs. Insome embodiments, the data lake 130 is implemented using the Hadoop®Distributed File System (HDFS™) from Apache® Software Foundation ofForest Hill, Md. HDFS™ is a highly scalable and distributed file systemthat can scale to thousands of cluster nodes, millions of files, andpetabytes of data. HDFS™ is optimized for batch processing where datalocations are exposed to allow computations to take place where the dataresides. HDFS™ provides a single namespace for an entire cluster toallow for data coherency in a write-once, read-many access model. Thatis, clients can only append to existing files in the node. In HDFS™,files are separated into blocks, which are typically 64 MB in size andare replicated in multiple data nodes. Clients access data directly fromdata nodes.

In some embodiments, the data mover 108 receives raw network traffic andcorresponding data from the collectors 106 and distributes or pushes thedata to the data lake 130. The data lake 130 can also receive and storeout-of-band data 114, such as statuses on power levels, networkavailability, server performance, temperature conditions, cage doorpositions, and other data from internal sources, and third party data116, such as security reports (e.g., provided by Cisco® Systems, Inc. ofSan Jose, Calif., Arbor Networks® of Burlington, Mass., Symantec® Corp.of Sunnyvale, Calif., Sophos® Group plc of Abingdon, England, Microsoft®Corp. of Seattle, Wash., Verizon® Communications, Inc. of New York,N.Y., among others), geolocation data, IP watch lists, Whois data,configuration management database (CMDB) or configuration managementsystem (CMS) as a service, and other data from external sources. Inother embodiments, the data lake 130 can instead fetch or pull rawtraffic and corresponding data from the collectors 106 and relevant datafrom the out-of-band data sources 114 and the third party data sources116. In yet other embodiments, the functionality of the collectors 106,the data mover 108, the out-of-band data sources 114, the third partydata sources 116, and the data lake 130 can be combined. Variouscombinations and configurations are possible as would be known to one ofordinary skill in the art.

Each component of the data lake 130 can perform certain processing ofthe raw network traffic data and/or other data (e.g., host data, processdata, user data, out-of-band data or third party data) to transform theraw data to a form useable by the elastic processing engines 150. Insome embodiments, the data lake 130 can include repositories for flowattributes 132, host and/or endpoint attributes 134, process attributes136, and policy attributes 138. In some embodiments, the data lake 130can also include repositories for VM or container attributes,application attributes, tenant attributes, network topology, applicationdependency maps, cluster attributes, etc.

The flow attributes 132 relate to information about flows traversing thenetwork. A flow is generally one or more packets sharing certainattributes that are sent within a network within a specified period oftime. The flow attributes 132 can include packet header fields such as asource address (e.g., Internet Protocol (IP) address, Media AccessControl (MAC) address, Domain Name System (DNS) name, or other networkaddress), source port, destination address, destination port, protocoltype, class of service, among other fields. The source address cancorrespond to a first endpoint (e.g., network device, physical server,virtual partition, etc.) of the network, and the destination address cancorrespond to a second endpoint, a multicast group, or a broadcastdomain. The flow attributes 132 can also include aggregate packet datasuch as flow start time, flow end time, number of packets for a flow,number of bytes for a flow, the union of TCP flags for a flow, amongother flow data.

The host and/or endpoint attributes 134 describe host and/or endpointdata for each flow, and can include host and/or endpoint name, networkaddress, operating system, CPU usage, network usage, disk space, ports,logged users, scheduled jobs, open files, and information regardingfiles and/or directories stored on a host and/or endpoint (e.g.,presence, absence, or modifications of log files, configuration files,device special files, or protected electronic information). Asdiscussed, in some embodiments, the host and/or endpoints attributes 134can also include the out-of-band data 114 regarding hosts such as powerlevel, temperature, and physical location (e.g., room, row, rack, cagedoor position, etc.) or the third party data 116 such as whether a hostand/or endpoint is on an IP watch list or otherwise associated with asecurity threat, Whois data, or geocoordinates. In some embodiments, theout-of-band data 114 and the third party data 116 can be associated byprocess, user, flow, or other more granular or less granular networkelement or network communication.

The process attributes 136 relate to process data corresponding to eachflow, and can include process name (e.g., bash, httpd, netstat, etc.),ID, parent process ID, path (e.g., /usr2/username/bin/, /usr/local/bin,/usr/bin, etc.), CPU utilization, memory utilization, memory address,scheduling information, nice value, flags, priority, status, start time,terminal type, CPU time taken by the process, the command that startedthe process, and information regarding a process owner (e.g., user name,ID, user's real name, e-mail address, user's groups, terminalinformation, login time, expiration date of login, idle time, andinformation regarding files and/or directories of the user).

The policy attributes 138 contain information relating to networkpolicies. Policies establish whether a particular flow is allowed ordenied by the network as well as a specific route by which a packettraverses the network. Policies can also be used to mark packets so thatcertain kinds of traffic receive differentiated service when used incombination with queuing techniques such as those based on priority,fairness, weighted fairness, token bucket, random early detection, roundrobin, among others. The policy attributes 138 can include policystatistics such as a number of times a policy was enforced or a numberof times a policy was not enforced. The policy attributes 138 can alsoinclude associations with network traffic data. For example, flows foundto be non-conformant can be linked or tagged with corresponding policiesto assist in the investigation of non-conformance.

The analytics engine 110 can include any number of engines 150,including for example, a flow engine 152 for identifying flows (e.g.,flow engine 152) or an attacks engine 154 for identify attacks to thenetwork. In some embodiments, the analytics engine can include aseparate distributed denial of service (DDoS) attack engine 155 forspecifically detecting DDoS attacks. In other embodiments, a DDoS attackengine can be a component or a sub-engine of a general attacks engine.In some embodiments, the attacks engine 154 and/or the DDoS engine 155can use machine learning techniques to identify security threats to anetwork. For example, the attacks engine 154 and/or the DDoS engine 155can be provided with examples of network states corresponding to anattack and network states corresponding to normal operation. The attacksengine 154 and/or the DDoS engine 155 can then analyze network trafficdata to recognize when the network is under attack. In some embodiments,the network can operate within a trusted environment for a time toestablish a baseline for normal network operation for the attacks engine154 and/or the DDoS.

The analytics engine 110 can further include a search engine 156. Thesearch engine 156 can be configured, for example to perform a structuredsearch, an NLP (Natural Language Processing) search, or a visual search.Data can be provided to the engines from one or more processingcomponents.

The analytics engine 110 can also include a policy engine 158 thatmanages network policy, including creating and/or importing policies,monitoring policy conformance and non-conformance, enforcing policy,simulating changes to policy or network elements affecting policy, amongother policy-related tasks.

The ADM module 140 can determine dependencies of applications of thenetwork. That is, particular patterns of traffic can correspond to anapplication, and the interconnectivity or dependencies of theapplication can be mapped to generate a graph for the application (i.e.,an application dependency mapping). In this context, an applicationrefers to a set of networking components that provides connectivity fora given set of workloads. For example, in a conventional three-tierarchitecture for a web application, first endpoints of the web tier,second endpoints of the application tier, and third endpoints of thedata tier make up the web application. The ADM module 140 can receiveinput data from various repositories of the data lake 130 (e.g., theflow attributes 132, the host and/or endpoint attributes 134, theprocess attributes 136, etc.). The ADM module 140 can analyze the inputdata to determine that there is first traffic flowing between externalendpoints on port 80 of the first endpoints corresponding to HypertextTransfer Protocol (HTTP) requests and responses. The input data can alsoindicate second traffic between first ports of the first endpoints andsecond ports of the second endpoints corresponding to application serverrequests and responses and third traffic flowing between third ports ofthe second endpoints and fourth ports of the third endpointscorresponding to database requests and responses. The ADM module 140 candefine an ADM for the web application as a three-tier applicationincluding a first EPG comprising the first endpoints, a second EPGcomprising the second endpoints, and a third EPG comprising the thirdendpoints.

The presentation module 112 can include an application programminginterface (API) or command line interface (CLI) 160, a securityinformation and event management (SIEM) interface 162, and a webfront-end 164. As the analytics engine 110 processes network traffic andcorresponding data and generates analytics data, the analytics data maynot be in a human-readable form or it can be too voluminous for a userto navigate. The presentation module 112 can take the analytics datagenerated by analytics engine 110 and further summarize, filter, andorganize the analytics data as well as create intuitive presentationsfor the analytics data.

In some embodiments, the API or CLI 160 can be implemented using Hadoop®Hive from Apache® for the back end, and Java® Database Connectivity(JDBC) from Oracle® Corporation of Redwood Shores, Calif., as an APIlayer. Hive is a data warehouse infrastructure that provides datasummarization and ad hoc querying. Hive provides a mechanism to querydata using a variation of structured query language (SQL) that is calledHiveQL. JDBC is an API for the programming language Java®, which defineshow a client can access a database.

In some embodiments, the SIEM interface 162 can be implemented usingHadoop® Kafka for the back end, and software provided by Splunk®, Inc.of San Francisco, Calif. as the SIEM platform. Kafka is a distributedmessaging system that is partitioned and replicated. Kafka uses theconcept of topics. Topics are feeds of messages in specific categories.In some embodiments, Kafka can take raw packet captures and telemetryinformation from the data mover 108 as input, and output messages to aSIEM platform, such as Splunk®. The Splunk® platform is utilized forsearching, monitoring, and analyzing machine-generated data.

In some embodiments, the web front-end 164 can be implemented usingsoftware provided by MongoDB®, Inc. of New York, N.Y. and Hadoop®ElasticSearch from Apache® for the back-end, and Ruby on Rails™ as theweb application framework. MongoDB® is a document-oriented NoSQLdatabase based on documents in the form of JavaScript® Object Notation(JSON) with dynamic schemas. ElasticSearch is a scalable and real-timesearch and analytics engine that provides domain-specific language (DSL)full querying based on JSON. Ruby on Rails™ is model-view-controller(MVC) framework that provides default structures for a database, a webservice, and web pages. Ruby on Rails™ relies on web standards such asJSON or extensible markup language (XML) for data transfer, andhypertext markup language (HTML), cascading style sheets, (CSS), andJavaScript® for display and user interfacing.

Although FIG. 1 illustrates an example configuration of the variouscomponents of a network traffic monitoring system, those of skill in theart will understand that the components of the network trafficmonitoring system 100 or any system described herein can be configuredin a number of different ways and can include any other type and numberof components. For example, the sensors 104, the collectors 106, thedata mover 108, and the data lake 130 can belong to one hardware and/orsoftware module or multiple separate modules. Other modules can also becombined into fewer components and/or further divided into morecomponents.

FIG. 2 illustrates an example of a network environment 200 in accordancewith an embodiment. In some embodiments, a network traffic monitoringsystem, such as the network traffic monitoring system 100 of FIG. 1, canbe implemented in the network environment 200. It should be understoodthat, for the network environment 200 and any environment discussedherein, there can be additional or fewer nodes, devices, links,networks, or components in similar or alternative configurations.Embodiments with different numbers and/or types of clients, networks,nodes, cloud components, servers, software components, devices, virtualor physical resources, configurations, topologies, services, appliances,deployments, or network devices are also contemplated herein. Further,the network environment 200 can include any number or type of resources,which can be accessed and utilized by clients or tenants. Theillustrations and examples provided herein are for clarity andsimplicity.

The network environment 200 can include a network fabric 202, a Layer 2(L2) network 204, a Layer 3 (L3) network 206, and servers 208 a, 208 b,208 c, 208 d, and 208 e (collectively, 208). The network fabric 202 caninclude spine switches 210 a, 210 b, 210 c, and 210 d (collectively,“210”) and leaf switches 212 a, 212 b, 212 c, 212 d, and 212 e(collectively, “212”). The spine switches 210 can connect to the leafswitches 212 in the network fabric 202. The leaf switches 212 caninclude access ports (or non-fabric ports) and fabric ports. The fabricports can provide uplinks to the spine switches 210, while the accessports can provide connectivity to endpoints (e.g., the servers 208),internal networks (e.g., the L2 network 204), or external networks(e.g., the L3 network 206).

The leaf switches 212 can reside at the edge of the network fabric 202,and can thus represent the physical network edge. For instance, in someembodiments, the leaf switches 212 d and 212 e operate as border leafswitches in communication with edge devices 214 located in the externalnetwork 206. The border leaf switches 212 d and 212 e can be used toconnect any type of external network device, service (e.g., firewall,deep packet inspector, traffic monitor, load balancer, etc.), or network(e.g., the L3 network 206) to the fabric 202.

Although the network fabric 202 is illustrated and described herein asan example leaf-spine architecture, one of ordinary skill in the artwill readily recognize that various embodiments can be implemented basedon any network topology, including any datacenter or cloud networkfabric. Indeed, other architectures, designs, infrastructures, andvariations are contemplated herein. For example, the principlesdisclosed herein are applicable to topologies including three-tier(including core, aggregation, and access levels), fat tree, mesh, bus,hub and spoke, etc. Thus, in some embodiments, the leaf switches 212 canbe top-of-rack switches configured according to a top-of-rackarchitecture. In other embodiments, the leaf switches 212 can beaggregation switches in any particular topology, such as end-of-row ormiddle-of-row topologies. In some embodiments, the leaf switches 212 canalso be implemented using aggregation switches.

Moreover, the topology illustrated in FIG. 2 and described herein isreadily scalable and can accommodate a large number of components, aswell as more complicated arrangements and configurations. For example,the network can include any number of fabrics 202, which can begeographically dispersed or located in the same geographic area. Thus,network nodes can be used in any suitable network topology, which caninclude any number of servers, virtual machines or containers, switches,routers, appliances, controllers, gateways, or other nodesinterconnected to form a large and complex network. Nodes can be coupledto other nodes or networks through one or more interfaces employing anysuitable wired or wireless connection, which provides a viable pathwayfor electronic communications.

Network communications in the network fabric 202 can flow through theleaf switches 212. In some embodiments, the leaf switches 212 canprovide endpoints (e.g., the servers 208), internal networks (e.g., theL2 network 204), or external networks (e.g., the L3 network 206) accessto the network fabric 202, and can connect the leaf switches 212 to eachother. In some embodiments, the leaf switches 212 can connect endpointgroups (EPGs) to the network fabric 202, internal networks (e.g., the L2network 204), and/or any external networks (e.g., the L3 network 206).EPGs are groupings of applications, or application components, and tiersfor implementing forwarding and policy logic. EPGs can allow forseparation of network policy, security, and forwarding from addressingby using logical application boundaries. EPGs can be used in the networkenvironment 200 for mapping applications in the network. For example,EPGs can comprise a grouping of endpoints in the network indicatingconnectivity and policy for applications.

As discussed, the servers 208 can connect to the network fabric 202 viathe leaf switches 212. For example, the servers 208 a and 208 b canconnect directly to the leaf switches 212 a and 212 b, which can connectthe servers 208 a and 208 b to the network fabric 202 and/or any of theother leaf switches. The servers 208 c and 208 d can connect to the leafswitches 212 b and 212 c via the L2 network 204. The servers 208 c and208 d and the L2 network 204 make up a local area network (LAN). LANscan connect nodes over dedicated private communications links located inthe same general physical location, such as a building or campus.

The WAN 206 can connect to the leaf switches 212 d or 212 e via the L3network 206. WANs can connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical light paths, synchronous optical networks (SONET), orsynchronous digital hierarchy (SDH) links. LANs and WANs can include L2and/or L3 networks and endpoints.

The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. The nodes typically communicate over the network byexchanging discrete frames or packets of data according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP). In this context, a protocol can refer to a set of rulesdefining how the nodes interact with each other. Computer networks canbe further interconnected by an intermediate network node, such as arouter, to extend the effective size of each network. The endpoints,e.g. the servers 208, can include any communication device or component,such as a computer, server, blade, hypervisor, virtual machine,container, process (e.g., running on a virtual machine), switch, router,gateway, host, device, external network, etc.

In some embodiments, the network environment 200 also includes a networkcontroller running on the host 208 a. The network controller isimplemented using the Application Policy Infrastructure Controller(APIC™) from Cisco®. The APIC™ provides a centralized point ofautomation and management, policy programming, application deployment,and health monitoring for the fabric 202. In some embodiments, the APIC™is operated as a replicated synchronized clustered controller. In otherembodiments, other configurations or software-defined networking (SDN)platforms can be utilized for managing the fabric 202.

In some embodiments, a physical server 208 can have instantiated thereona hypervisor 216 for creating and running one or more virtual switches(not shown) and one or more virtual machines 218, as shown for the host208 b. In other embodiments, physical servers can run a shared kernelfor hosting containers. In yet other embodiments, the physical server208 can run other software for supporting other virtual partitioningapproaches. Networks in accordance with various embodiments can includeany number of physical servers hosting any number of virtual machines,containers, or other virtual partitions. Hosts can also compriseblade/physical servers without virtual machines, containers, or othervirtual partitions, such as the servers 208 a, 208 c, 208 d, and 208 e.

The network environment 200 can also integrate a network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1. For example, the network traffic monitoring system ofFIG. 2 includes sensors 220 a, 220 b, 220 c, and 220 d (collectively,“220”), collectors 222, and an analytics engine, such as the analyticsengine 110 of FIG. 1, executing on the server 208 e. The analyticsengine can receive and process network traffic data collected by thecollectors 222 and detected by the sensors 220 placed on nodes locatedthroughout the network environment 200. Although the analytics engine208 e is shown to be a standalone network appliance in FIG. 2, it willbe appreciated that the analytics engine 208 e can also be implementedas a virtual partition (e.g., VM or container) that can be distributedonto a host or cluster of hosts, software as a service (SaaS), or othersuitable method of distribution. In some embodiments, the sensors 220run on the leaf switches 212 (e.g., the sensor 220 a), the hosts (e.g.,the sensor 220 b), the hypervisor 216 (e.g., the sensor 220 c), and theVMs 218 (e.g., the sensor 220 d). In other embodiments, the sensors 220can also run on the spine switches 210, virtual switches, serviceappliances (e.g., firewall, deep packet inspector, traffic monitor, loadbalancer, etc.) and in between network elements. In some embodiments,sensors 220 can be located at each (or nearly every) network componentto capture granular packet statistics and data at each hop of datatransmission. In other embodiments, the sensors 220 may not be installedin all components or portions of the network (e.g., shared hostingenvironment in which customers have exclusive control of some virtualmachines).

As shown in FIG. 2, a host can include multiple sensors 220 running onthe host (e.g., the host sensor 220 b) and various components of thehost (e.g., the hypervisor sensor 220 c and the VM sensor 220 d) so thatall (or substantially all) packets traversing the network environment200 can be monitored. For example, if one of the VMs 218 running on thehost 208 b receives a first packet from the WAN 206, the first packetcan pass through the border leaf switch 212 d, the spine switch 210 b,the leaf switch 212 b, the host 208 b, the hypervisor 216, and the VM.Since all or nearly all of these components contain a respective sensor,the first packet will likely be identified and reported to one of thecollectors 222. As another example, if a second packet is transmittedfrom one of the VMs 218 running on the host 208 b to the host 208 d,sensors installed along the data path, such as at the VM 218, thehypervisor 216, the host 208 b, the leaf switch 212 b, and the host 208d will likely result in capture of metadata from the second packet.

Currently, sensors, e.g. such as those of the network traffic monitoringsystem 100, deployed in a network can be used to gather network trafficdata related to nodes operating in the network. The network traffic datacan include metadata relating to a packet, a collection of packets, aflow, a bidirectional flow, a group of flows, a session, or a networkcommunication of another granularity. That is, the network traffic datacan generally include any information describing communication on alllayers of the Open Systems Interconnection (OSI) model. For example, thenetwork traffic data can include source/destination MAC address,source/destination IP address, protocol, port number, etc. In someembodiments, the network traffic data can also include summaries ofnetwork activity or other network statistics such as number of packets,number of bytes, number of flows, bandwidth usage, response time,latency, packet loss, jitter, and other network statistics.

Gathered network traffic data can be analyzed to provide insights intothe operation of the nodes in the network, otherwise referred to asanalytics. In particular, discovered application or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, and network flows can be determined for the network using thenetwork traffic data.

Sensors deployed in a network can be used to gather network traffic dataon a client and server level of granularity. For example, networktraffic data can be gathered for determining which clients arecommunicating which servers and vice versa. However, sensors are notcurrently deployed or integrated with systems to gather network trafficdata for different segments of traffic flows forming the traffic flowsbetween a server and a client. Specifically, current sensors gathernetwork traffic data as traffic flows directly between a client and aserver while ignoring which nodes, e.g. middleboxes, the traffic flowsactually pass through in passing between a server and a client. Morespecifically, current sensors are not configured to gather networktraffic data as traffic flows pass through multiple middleboxes betweena server and a client. This effectively treats the network environmentbetween servers and clients as a black box and leads to gaps in networktraffic data and traffic flows indicated by the network traffic data.

In turn, such gaps in network traffic data and corresponding trafficflows can lead to deficiencies in diagnosing problems within a networkenvironment. For example, a problem stemming from an incorrectlyconfigured middlebox might be diagnosed as occurring at a client as theflow between the client and a server is treated as a black box eventhough it actually passes through one or more middleboxes. In anotherexample, gaps in network traffic data between a server and a client canlead to an inability to determine whether policies are correctlyenforced at middleboxes between the server and the client. Theretherefore exist needs for systems, methods, and computer-readable mediafor generating network traffic data at nodes between servers andclients, e.g. at multiple middleboxes in a chain of middleboxes betweenthe servers and clients. In particular, there exist needs for systems,methods, and computer-readable media for stitching together trafficflows that pass through multiple nodes between servers and clients togenerate more complete and detailed traffic flows, e.g. between theservers and the clients.

The present includes systems, methods, and computer-readable media forstitching traffic flow segments across nodes, e.g. middleboxes, in anetwork environment to form a stitched traffic flow through the nodes inthe network environment. In particular, flow records can be collected oftraffic flow segments at both a first middlebox and a second middleboxin a network environment corresponding to one or more traffic flowspassing through either or both the first middlebox and the secondmiddlebox. The flow records can include one or more transactionidentifiers assigned to the traffic flows. Sources and destinations ofthe traffic flow segments in the network environment with respect toeither or both the first middlebox and the second middlebox can beidentified using the flow records. A subset of the traffic flow segmentsto can be stitched together to form a first stitched traffic flow at thefirst middlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and thesources and destination of the traffic flow segments in the networkenvironment with respect to the first middlebox. Additionally, anothersubset of the traffic flow segments can be stitched together to form asecond stitched traffic flow at the second middlebox in the networkenvironment based on the one or more transaction identifiers assigned tothe traffic flow segments and the sources and destination of the trafficflow segments in the network environment with respect to the secondmiddlebox. The different subsets of the traffic flow segments used togenerate the first stitched traffic flow and the second stitched trafficflow can include common traffic flow segments. The first stitchedtraffic flow and the second stitched traffic flow can be stitchedtogether to form a cross-middlebox stitched traffic flow across thefirst middlebox and the second middlebox. Subsequently, thecross-middlebox stitched traffic flow can be incorporated as part ofnetwork traffic data for the network environment.

FIG. 3 depicts a diagram of an example network environment 300 forstitching together traffic flow segments across multiple nodes between aclient and a server. The network environment 300 shown in FIG. 3includes a client 302, a server 304, a first middlebox 306-1, and asecond middlebox 306-2 (herein referred to as “the middleboxes 306”).The client 302 and the server 304 can exchange data. More specifically,the client 302 and the server 304 can exchange data as part of theclient 302 accessing network services in the network environment 300 andas part of the server 304 providing the client 302 access to networkservices in the network environment 300. For example, the client 302 cansend a request for data that can ultimately be delivered to the server304 and the server 304 can ultimately send the data back to the client302 as part of a reply to the request.

The client 302 and the server 304 can exchange data as part of one ormore traffic flows. A traffic flow can be unidirectional orbidirectional. For example, a traffic flow can include the client 302sending a request that is ultimately received at the server 304. Viceversa, a traffic flow can include the server 304 sending a response thatis ultimately received at the client 302. In another example, a trafficflow can include both the client 302 sending a request that isultimately received at the server 304 and the server 304 sending aresponse to the request that is ultimately received at the client 302.Traffic flows between the client 302 and the server 304 can form part ofa traffic flow including the client 302 and other sources/destinationsat different network nodes, e.g. separate from the server 304 and theclient 302, within a network environment. For example, traffic flowsbetween the client 302 and the server 304 can form part of an overalltraffic flow between the client 302 and a network node within a networkenvironment that is ultimately accessed through the server 304 and anetwork fabric.

Further, the client 302 and the server 304 can exchange data as part ofone or more transactions. A transaction can include a requestoriginating at the client 302 and ultimately sent to the server 304.Further, a transaction can include a response to the request originatingat the server 304 and ultimately send to the client 302. Vice versa, atransaction can include a request that originates at the server 304 andis ultimately sent to the client 302 and a response to the request thatoriginates at the client 302 and is ultimately send to the server 304. Atransaction can be associated with a completed flow. Specifically, acompleted flow of a transaction can include a request that originates atthe client 302 and is sent to the server 304 and a response to therequest that originates at the server 304 and is sent to the client 302.

In the example network environment 300 shown in FIG. 3, the client 302and the server 304 can exchange data or otherwise communicate throughthe middleboxes 306. A middlebox, as used herein, is an applicablenetworking device for controlling network traffic in the networkenvironment 300 that passes through the middlebox. More specifically, amiddlebox can be an applicable networking device for filtering,inspecting, modifying, or otherwise controlling traffic that passesthrough the middlebox for purposes other than actually forwarding thetraffic to an intended destination. For example, a middlebox can includea firewall, an intrusion detection system, a network address translator,a WAN optimizer, and a load balancer.

In supporting exchange of data between the client 302 and the server304, different portions of traffic flows, otherwise referred to astraffic flow segments, can be created at the middleboxes 306 between theclient 302 and the server 304. Specifically, the first middlebox 306-1can receive data from the client 302 in a first traffic flow segment308-1. Subsequently, the first middlebox 306-1 can provide data receivedfrom the client 302, e.g. through the first traffic flow segment 308-1,to the second middlebox 306-2 as part of a second traffic flow segment308-2. The second middlebox 306-2 can then provide the data receivedfrom the client 302 through the first middlebox 306-1, e.g. through thesecond traffic flow segment 308-2, to the server 304 as part of a thirdtraffic flow segment 308-3. Similarly, the second middlebox 306-2 canreceive data from the server 304 in a fourth traffic flow segment 308-4.The second middlebox 306-2 can provide data received from the server304, e.g. in the fourth traffic flow segment 308-4, to the firstmiddlebox 306-1 in a fifth traffic flow segment 308-5. Subsequently, thefirst middlebox 306-1 can provide data from the server 304 and receivedfrom the second middlebox in the fifth traffic flow segment 308-5 to theclient 302 as part of a sixth traffic flow segment 308-6.

All or an applicable combination of the first traffic flow segment308-1, the second traffic flow segment 308-2, the third traffic flowsegment 308-3, the fourth traffic flow segment 308-4, the fifth trafficflow segment 308-5, and the sixth traffic flow segment 308-6(collectively referred to as the “traffic flow segments 308”) can formpart of a single traffic flow. For example, the first traffic flowsegment 308-1, the second traffic flow segment 308-2, and the thirdtraffic flow segment 308-3 can be used to transmit a request from theclient 302 to the server 304 and combine to form a single traffic flowbetween the client 302 and the server 304. In another example, thefirst, second, and third traffic flow segments 308-1, 308-2, 308-3 canform a request transmitted to the server 304 and the fourth, fifth, andsixth traffic flow segments 308-4, 308-5, and 308-6 can form a responseto the request. Further in the example, the traffic flow segments 308including both the request and the response to the request can form asingle traffic flow between the client 302 and the server 304.

The traffic flow segments 308 can be associated with or otherwiseassigned one or more transaction identifiers. More specifically, atransaction identifier can be uniquely associated with a single trafficflow passing through both the first middlebox 306-1 and the secondmiddlebox 306-2. Subsequently, all or a combination of the traffic flowsegments 308 can be associated with a transaction identifier uniquelyassociated with a traffic flow formed by all or the combination of thetraffic flow segments 308. For example, the traffic flow segments 308can form a single traffic flow between the client 302 and the server 304and each be assigned a single transaction identifier for the trafficflow. In another example, the first traffic flow segment 308-1, thesecond traffic flow segment 308-2, and the third traffic flow segment308-3 can form a first traffic flow and the fourth traffic flow segment308-4, the fifth traffic flow segment 308-5, and the sixth traffic flowsegment 308-6 can form a second traffic flow. Subsequently, atransaction identifier uniquely associated with the first traffic flowcan be assigned to the first traffic flow segment 308-1, the secondtraffic flow segment 308-2, and the third traffic flow segment 308-3,while a transaction identifier uniquely associated with the secondtraffic flow can be assigned to the fourth traffic flow segment 308-4,the fifth traffic flow segment 308-5, and the sixth traffic flow segment308-6.

While the client 302 and the server 304 are shown as communicatingthrough the middleboxes 306, in the example environment shown in FIG. 3,either or both of the client 302 and the server 304 can be replaced withanother middlebox. For example, the server 304 and another middlebox cancommunicate with each other through the middleboxes 306. In anotherexample, the client 302 and another middlebox can communicate with eachother through the middleboxes 306.

The example network environment 300 shown in FIG. 3 includes a firstmiddlebox traffic flow segment collector 310-1, a second middleboxtraffic flow segment collector 310-2 (herein referred to as “middleboxtraffic flow segment collectors 310”), and a middlebox traffic flowstitching system 312. The middlebox traffic flow segment collectors 310function to collect flow records for the middleboxes 306. In collectingflow records for the middleboxes 306, the middlebox traffic flow segmentcollectors 310 can be implemented as an appliance. Further, themiddlebox traffic flow segment collectors 310 can be implemented, atleast in part, at the middleboxes 306. Additionally, the middleboxtraffic flow segment collectors 310 can be implemented, at least inpart, remote from the middleboxes 306. For example, the middleboxtraffic flow segment collectors 310 can be implemented on virtualmachines either residing at the middleboxes 306 or remote from themiddleboxes 306. While, the example network environment 300 in FIG. 3 isshown to include separate middlebox traffic flow segment collectors, invarious embodiments, the environment 300 can only include a singlemiddlebox traffic flow segment collector. More specifically, theenvironment 300 can include a single middlebox traffic flow segmentcollector that collects flow records from both the first middlebox 306-1and the second middlebox 306-2.

Each of the middlebox traffic flow segment collectors 310 can collectflow records for a corresponding middlebox. For example, the firstmiddlebox traffic flow segment collector 310-1 can collect flow recordsfor the first middlebox 306-1. Further in the example, the secondmiddlebox traffic flow segment collector 310-2 can collect flow recordsfor the second middlebox 306-2.

Flow records of a middlebox can include applicable data related totraffic segments flowing through the middlebox. Specifically, flowrecords of a middlebox can include one or a combination of a source ofdata transmitted in a traffic flow segment, a destination for datatransmitted in a traffic flow segment, a transaction identifier assignedto a traffic flow segment. More specifically, flow records of amiddlebox can include one or a combination of an address, e.g. IPaddress of a source or a destination, and an identification of a port ata source or a destination, e.g. an ephemeral port, a virtual IP (hereinreferred to as “VIP”) port, a subnet IP (herein referred to as “SNIP”)port, or a server port. For example, flow records collected by the firstmiddlebox traffic flow segment collector 310-1 for the first trafficflow segment 308-1 and the second traffic flow segment 308-2 can includea unique identifier associated with a traffic flow formed by thesegments 308-1 and 308-2 and assigned to the first and second trafficflow segments 308-1 and 308-2. Further in the example, the flow recordscan include an IP address of the client 302 where the first traffic flowsegment 308-1 originates and a VIP port at the first middlebox 306-1where the first traffic flow segment 308-1 is received. Still further inthe example, the flow records can include an SNIP port at the firstmiddlebox 306-1 where the second traffic flow segment 308-2 originatesand a VIP port at the second middlebox 306-2 where the second trafficflow segment 308-2 is received, e.g. for purposes of load balancing.

Data included in flow records of corresponding traffic flow segmentspassing through a middlebox can depend on whether the traffic flowsegments originate at a corresponding middlebox of the middleboxes 306or ends at a corresponding middlebox of the middlebox 306. For example,a flow record for the first traffic flow segment 308-1 that is collectedby the first middlebox traffic flow segment collector 310-1 can includea unique transaction identifier and indicate that the first traffic flowsegment starts at the client 302 and ends at the first middlebox 306-1.Similarly, a flow record for the second traffic flow segment 308-2 caninclude the unique transaction identifier, which is also assigned to thefirst traffic flow segment 308-1, as well as an indication that thesecond traffic flow segment starts at the first middlebox 306-1 and endsat the second middlebox 306-2. Accordingly, flow records for trafficflow segments passing through the middleboxes 306 are each rooted at themiddleboxes 306, e.g. by including an indication of one of themiddleboxes 306 as a source or a destination of the traffic flowsegments. Specifically, as traffic flow segments are rooted at themiddleboxes 306, flow records for the traffic flow segments passingthrough the middleboxes 306 each either begin or end at the middleboxes306.

The middleboxes 306 can generate flow records for traffic flow segmentspassing through the middleboxes 306. More specifically, the middleboxes306 can associate or otherwise assign a unique transaction identifier totraffic flow segments as part of creating flow records for the trafficflow segments. For example, the first middlebox 306-1 can assign a TID1of a consumer request to the first traffic flow segment 308-1 as part ofcreating a flow record, e.g. for the first traffic flow segment 308-1.Further in the example, the first middlebox 306-1 can determine to sendthe consumer request to the second middlebox 306-2 and/or the server304, e.g. as part of load balancing. Still further in the example, thefirst middlebox 306-1 can assign the TID1 of the consumer request to thesecond traffic flow segment 308-1 as the consumer request is transmittedto the second middlebox 306-2 through the second traffic flow segment308-2, e.g. as part of the load balancing. Subsequently, the middleboxes306 can export the generated flow segments for traffic flow segmentspassing through the middleboxes 306.

Additionally, the middleboxes 306 can modify a flow record for a trafficflow segment by associating the traffic flow segment with a transactionidentifier as part of exporting the flow record. For example, themiddleboxes 306 can determine to export a flow record for a traffic flowsegment. Subsequently, before exporting the flow record, the middleboxes306 can associate the traffic flow segment with a transaction identifierand subsequently modify the flow record to include the transactionidentifier. The middleboxes 306 can then export the modified flow recordincluding the transaction identifier.

The middlebox traffic flow segment collectors 310 can collect flowrecords from the middleboxes 306 as the flow records are completed orotherwise generated by the middleboxes 306. Specifically, themiddleboxes 306 can generate and/or export flow records for traffic flowsegments as all or portions of corresponding traffic flows actually passthrough the middleboxes 306. More specifically, the first middlebox306-1 can create and export traffic flow records as either or both thefirst traffic flow segment 308-1 and the second traffic flow segment308-2 are completed at the first middlebox 306-1. Additionally, themiddleboxes 306 can generate and/or export flow records for traffic flowsegments once a corresponding traffic flow formed by the segments iscompleted through the middleboxes 306. For example, the first middlebox306-1 can create and export traffic flow records for the traffic flowsegments 308-1, 308-2, 308-5, and 308-6 once all of the traffic flowsegments are transmitted to complete a traffic flow through the firstmiddlebox 306-1. Further in the example, the first middlebox 306-1 canrecognize that a consumer to producer flow is complete, e.g. the first,second, and third traffic flow segments 308-1, 308-2, 308-3 are completeor all of the traffic flow segments 308 are completed, and subsequentlythe first middlebox 306-1 can export one or more corresponding flowrecords to the first middlebox traffic flow segment collector 310-1.

The middlebox traffic flow segment collectors 310 can receive orotherwise collect traffic flow records from the middleboxes 306according to an applicable protocol for exporting flow records, e.g.from a middlebox. More specifically, the middleboxes 306 can export flowrecords to the middlebox traffic flow segment collectors 310 accordingto an applicable protocol for exporting flow records, e.g. from amiddlebox. For example, the middleboxes 306 can export flow records tothe middlebox traffic flow segment collector 310 using an InternetProtocol Flow Information Export (herein referred to as “IPFIX”)protocol. In another example, the middleboxes 306 can export flowrecords to the middlebox traffic flow segment collectors 310 using aNetFlow Packet transport protocol.

While flow records can indicate traffic flow segments are rooted at themiddleboxes 306, the flow records for traffic segments passing throughthe middlebox 306 can fail to link the traffic flow segments, e.g.through the middleboxes 306. Specifically, flow records for the firsttraffic flow segment 308-1 can indicate that the first traffic flowsegment 308-1 ends at the first middlebox 306-1 while flow records forthe second traffic flow segment 308-2 can indicate that the secondtraffic flow segment 308-2 begins at the first middlebox 306-1, whilefailing to link the first and second traffic flow segments 308-1 and308-2. Further, flow records for the first traffic flow segment 308-1can indicate that the third traffic flow segment 308-3 begins at thesecond middlebox 306-2, while failing to link the first and secondtraffic flow segments 308-1 and 308-2 with the third traffic flowsegment 308-3.

Failing to link traffic flow segments through the middleboxes 306 isproblematic in synchronizing or otherwise identifying how the server 304and the client 302 communicate through the middleboxes 306.Specifically, failing to link traffic flow segments through themiddleboxes 306 leads to a view from a server-side perspective that allflows end in the second middlebox 306-2. Similarly, failing to linktraffic flow segments through the middleboxes 306 leads to a view from aclient-side perspective that all flows end in the first middlebox 306-1.This can correspond to gaps in mapping traffic flows between the client302 and the server 304, e.g. the middleboxes 306 are treated like ablack box without linking the client 302 with the server 304. In turn,this can lead to deficiencies in diagnosing problems within the networkenvironment 300. For example, a failed policy check at the firstmiddlebox 306-1 can mistakenly be identified as happening at the client302 even though it actually occurs at the first middlebox 306-1.Specifically, the failed policy check can be triggered by a failure ofthe first middlebox 306-1 to route data according to the policy betweenthe client 302 and the server 304, however since the traffic flowsegments rotted at the first middlebox 306-1 are not linked with thetraffic flow segments rotted at the second middlebox 306-2, the failedpolicy check can be identified from a traffic flow segment as occurringat the client 302 instead of the first middlebox 306-1.

The middlebox traffic flow stitching system 312 functions to stitchtogether traffic flow segments passing through the middleboxes 306 tocreate stitched traffic flows at the middleboxes 306. For example, themiddlebox traffic flow stitching system 312 can stitch together thefirst traffic flow segment 308-1, the second traffic flow segment 308-2,the fifth traffic flow segment 308-5, and the sixth traffic flow segment308-6 to form a stitched traffic flow at the first middlebox 306-1.Similarly, the middlebox traffic flow stitching system 312 can stitchtogether the second traffic flow segment 308-2, the third traffic flowsegment 308-3, the fourth traffic flow segment 308-4, and the fifthtraffic flow segment 308-5 to form a stitched traffic flow at the secondmiddlebox 306-2. Stitched traffic flows can be represented or otherwiseused to create corresponding flow data. Flow data for a stitched trafficflow can include identifiers of stitched traffic flow segments, e.g.identifiers of sources and destinations of the traffic flow segments,and transactions associated with the stitched traffic flow segments,e.g. associated transaction identifiers.

Further, the middlebox traffic flow stitching system 312 can stitchtogether stitched traffic flows to form a cross-middlebox stitchedtraffic flow. A cross-middlebox stitched traffic flow can includetraffic flow segments stitched together across a plurality ofcorresponding middleboxes that the traffic flow segments pass through orare otherwise rooted at to create a stitched traffic flow across themiddleboxes. For example, the middlebox traffic flow stitching system312 can stitch together the first, second, fifth, and sixth traffic flowsegments 308-1, 308-2, 308-5, and 308-6 to form a first stitched trafficflow at the first middlebox 306-1. Further in the example, the middleboxtraffic flow stitching system 312 can stitch together the second, third,fourth, and fifth traffic flow segments 308-2, 308-3, 308-4, and 308-5to form a second stitched traffic flow at the second middlebox 306-2.Still further in the example, the middlebox traffic flow stitchingsystem 312 can stitch together the first stitched traffic flow and thesecond stitched traffic flow to form a cross-middlebox stitched trafficflow between the client 302 and the server 304 that spans across thefirst middlebox 306-1 and the second middlebox 306-2.

In stitching together traffic flow segments at the middleboxes 306 tocreate stitched traffic flows and corresponding cross-middlebox stitchedtraffic flows, the middleboxes 306 can no longer function as black boxeswith respect to traffic flows passing through the middleboxes 306.Specifically, from both a server side perspective and a client sideperspective, a traffic flow can be viewed as actually passing throughthe middleboxes 306 to the client 302 or the server 304 and not just astraffic flow segments that only originate at or end at the middleboxes306. This is advantageous as it allows for more complete and insightfulnetwork monitoring, leading to more accurate problem diagnosing andsolving. For example, as traffic flows are seen as actually passingthrough the middleboxes 306, misconfigurations of the middleboxes 306can be identified from the traffic flows, e.g. as part of monitoringnetwork environments.

Traffic flows at the middleboxes 306 stitched together by the middleboxtraffic flow stitching system 312 can be used to enforce policies atmiddleboxes, including the middleboxes 306. Specifically, stitchedtraffic flows and corresponding cross-middlebox stitched traffic flowsgenerated by the middlebox traffic flow stitching system 312 can be usedto identify dependencies between either or both servers and clients. Forexample, cross-middlebox stitched traffic flows generated by themiddlebox traffic flow stitching system 312 can be used to generateapplication dependency mappings between different applications atservers and clients. Subsequently, policies can be set and subsequentlyenforced at the middleboxes 306 based on dependencies identified usingthe cross-middlebox stitched traffic flows generated by the middleboxtraffic flow stitching system 312. For example, a policy to load balancecommunications between clients and servers can be identified accordingto an application dependency mapping between the clients and the serversidentified through cross-middlebox stitched traffic flows at themiddleboxes 306. Further in the example, the policy can subsequently beenforced at the middleboxes 306 to provide load balancing between theclients and the servers.

The middlebox traffic flow stitching system 312 can stitch togethertraffic flow segments passing through the middleboxes 306 based on flowrecords collected from the middleboxes 306. Specifically, the middleboxtraffic flow stitching system 312 can stitch together traffic flowsegments based on flow records collected by the middlebox traffic flowsegment collectors 310 from the middleboxes 306. In using flow recordsto stitch together traffic flow segments, the middlebox traffic flowstitching system 312 can stitch together traffic flow segments based ontransaction identifiers assigned to the traffic flow segments, asindicated by the flow records. Specifically, the middlebox traffic flowstitching system 312 can stitch together traffic flow segments that areassigned the same transaction identifiers. For example, the traffic flowsegments 308 can all have the same assigned transaction identifier, andthe middlebox traffic flow stitching system 312 can stitch together thetraffic flow segments 308 to form corresponding first and secondstitched traffic flows based on the shared transaction identifier.Further in the example, the middlebox traffic flow stitching system 312can stitch together the traffic flow segments 308 to form acorresponding first stitched traffic flow about the first middlebox306-1 and a corresponding second stitched traffic flow about the secondmiddlebox 306-2.

The middlebox traffic flow stitching system 312 can stitch togetherstitched traffic flows to form a cross-middlebox stitched traffic flowbased on flow records collected from the middleboxes 306. Specifically,the middlebox traffic flow stitching system 312 can stitch togetherstitched traffic flows based on one or more transaction identifiersincluded as part of the flow records to form a cross-middlebox stitchedtraffic flow. More specifically, the middlebox traffic flow stitchingsystem 312 can stitch together stitched traffic flows based on one ormore transaction identifiers associated with the traffic flow segmentsand used to generate the stitched traffic flows from the traffic flowsegments. For example, the middlebox traffic flow stitching system 312can stitch together the traffic flow segments 308 based on a transactionidentifier associated with the traffic flow segments 308 to form a firststitched traffic flow at the first middlebox 306-1 and a second stitchedtraffic flow at the second middlebox 306-2. Subsequently, the

Further, the middlebox traffic flow stitching system 312 can identifyflow directions of traffic flow segments passing through the middlebox306 using flow records collected from the middlebox 306 by the middleboxtraffic flow segment collector 310. Specifically, the middlebox trafficflow stitching system 312 can identify flow directions of traffic flowsegments with respect to the middlebox 306 using flow records collectedfrom the middlebox 306. For example, the middlebox traffic flowstitching system 312 can use flow records from the middlebox 306 toidentify the fourth traffic flow segment 308-4 flows from the middlebox306 to the client 302. The middlebox traffic flow stitching system 312can use identified sources and destinations of traffic flow segments, asindicated by flow records, to identify flow directions of the trafficflow segments. For example, the middlebox traffic flow stitching system312 can determine the first traffic flow segment 308-1 flows from theclient 302 to the middlebox 306 based on an identification of a clientIP address as the source of the first traffic flow segment 308-1 and anidentification of a VIP port at the middlebox 306.

In stitching together traffic flow segments based on flow records, themiddlebox traffic flow stitching system 312 can stitch together thetraffic flow segments based on directions of the traffic flow segmentsidentified from the flow records. For example, the middlebox trafficflow stitching system 312 can stitch together the first traffic flowsegment 308-1 with the second traffic flow segment 308-2 based on theidentified direction of the first and second traffic flow segments 308-1and 308-2 from the client 302 towards the server 304. Additionally, institching together traffic flow segments based on flow records, themiddlebox traffic flow stitching system 312 can stitch together thetraffic flow segments based on directions of the traffic flow segmentsand also transaction identifiers assigned to the traffic flow segments.For example, the middlebox traffic flow stitching system 312 can stitchthe third and fourth traffic flow segments 308-3 and 308-4 togetherbased on the segments having the same transaction identifier and theshared direction of the segments from the server 304 to the client 302.

The middlebox traffic flow stitching system 312 can stitch togethertraffic flow segments in an order based on flow directions of thetraffic flow segments. More specifically, the middlebox traffic flowstitching system 312 can use a shared transaction identifier todetermine traffic flow segments to stitch together, and stitch thetraffic flow segments in a specific order based on flow directions ofthe traffic flow segments to form a stitched traffic flow. For example,the middlebox traffic flow stitching system 312 can determine to stitchthe traffic flow segments 308 based on a shared transaction identifierassigned to the traffic flow segments 308. Further in the example, themiddlebox traffic flow stitching system 312 can determine to stitch thesecond traffic flow segment 308-2 after the first traffic flow segment308-1, stitch the fifth traffic flow segment 308-5 after the secondtraffic flow segment 308-2, and stitch the sixth traffic flow segment308-6 after the fifth traffic flow segment 308-5, based on correspondingidentified flow directions of the flow segments 308, e.g. with respectto the middlebox 306.

Further, the middlebox traffic flow stitching system 312 can stitchtogether stitched traffic flows to form cross-middlebox stitched trafficflows based on directions of traffic flow segments used to form thestitched traffic flows. For example, the middlebox traffic flowstitching system 312 can stitch together a stitched traffic flow at thefirst middlebox 306-1 with a stitched traffic flow at the secondmiddlebox 306-2 based on the second traffic flow segment 308-2 having adirection from the first middlebox 306-1 to the second middlebox 306-2.Similarly, the middlebox traffic flow stitching system 312 can stitchtogether a stitched traffic flow at the second middlebox 306-2 with astitched traffic flow at the first middlebox 306-1 based on the fifthtraffic flow segment having a direction from the second middlebox 306-2to the first middlebox 306-1.

Additionally, the middlebox traffic flow stitching system 312 can stitchtogether stitched traffic flows to form cross-middlebox stitched trafficflows based on common traffic flow segments. Specifically, the middleboxtraffic flow stitching system 312 can stitch together stitched trafficflows to form cross-middlebox stitched traffic flows if the stitchedtraffic flows share one or more common traffic flow segments. A commontraffic flow segment is a traffic flow segment that is used to form twoseparate stitched traffic flows. For example, the second traffic flowsegment 308-2 can be a common traffic flow segment for a stitchedtraffic flow at the first middlebox 306-1 and a stitched traffic flow atthe second middlebox 306-2. Further, the middlebox traffic flowstitching system 312 can stitch together flows according to positions ofcommon traffic flow segments in the stitched traffic flows. For example,based on the position of the second traffic flow segment 308-2 in afirst stitched traffic flow at the first middlebox 306-1 and a secondstitched traffic flow at the second middlebox 306-2, the middleboxtraffic flow stitching system 312 can stitch the second stitched trafficflow after the first stitched traffic flow.

The middlebox traffic flow stitching system 312 can incorporate eitheror both stitched traffic flows through the middleboxes 306 andcross-middlebox stitched traffic flows as part of network traffic datafor the network environment. For example, the middlebox traffic flowstitching system 312 can include stitched traffic flows through themiddleboxes 306 and cross-middlebox stitched traffic flows with othertraffic flows in the network environment, e.g. from servers to nodes ina network fabric. In another example, the middlebox traffic flowstitching system 312 can update network traffic data to indicate acompleted flow between a client and a server passes between the serverand the client through multiple middleboxes, e.g. as indicated by across-middlebox stitched traffic flow.

The middlebox traffic flow stitching system 312 can incorporate stitchedtraffic flows and cross-middlebox stitched traffic flows as part ofnetwork traffic data generated by an applicable network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1. In incorporating stitched traffic flows andcross-middlebox stitched traffic flows with network traffic datagenerated by a network traffic monitoring system, all or portions of themiddlebox traffic flow stitching system 312 can be integrated at thenetwork traffic monitoring system. For example, a portion of themiddlebox traffic flow stitching system 312 implemented at a networktraffic monitoring system can received stitched traffic flows andcross-middlebox stitched traffic flows from a portion of the middleboxtraffic flow stitching system 312 implemented at the middlebox trafficflow segment collectors 310. Subsequently, the middlebox traffic flowstitching system 312, e.g. implemented at the network traffic monitoringsystem, can incorporate the stitched traffic flows and thecross-middlebox stitched traffic flows into network traffic datagenerated by the network traffic monitoring system.

In incorporating stitched traffic flows and cross-middlebox stitchedtraffic flows into network traffic data, the middlebox traffic flowstitching system 312 can extend network traffic flows in the networktraffic data based on the stitched traffic flows and cross-middleboxstitched traffic flows. Specifically, the middlebox traffic flowstitching system 312 can stitch already stitched traffic flows extendingthrough the middleboxes 306 to the client with other stitched trafficflows extending into the network environment 300. More specifically, themiddlebox traffic flow stitching system 312 can stitch already stitchedtraffic flows through the middleboxes 306 with other traffic flows thatextend from the server 304 to other servers or nodes in the networkenvironment 300. For example, the middlebox traffic flow stitchingsystem 312 can stitch together a traffic flow extending from a networkfabric to the server 304 with a stitched traffic flow through themiddleboxes 306 to the client 302. This can create a completed trafficflow from the network fabric to the client 302 through the middleboxes306.

FIG. 4 illustrates a flowchart for an example method of forming across-middlebox stitched traffic flow across middleboxes. The methodshown in FIG. 4 is provided by way of example, as there are a variety ofways to carry out the method. Additionally, while the example method isillustrated with a particular order of blocks, those of ordinary skillin the art will appreciate that FIG. 4 and the blocks shown therein canbe executed in any order and can include fewer or more blocks thanillustrated.

Each block shown in FIG. 4 represents one or more steps, processes,methods or routines in the method. For the sake of clarity andexplanation purposes, the blocks in FIG. 4 are described with referenceto the network environment shown in FIG. 3.

At step 400, the middlebox traffic flow segment collectors 310 collectsflow records of traffic flow segments at first and second middleboxes ina network environment corresponding to one or more traffic flows passingthrough the middleboxes. The flow records can include one or moretransaction identifiers assigned to the traffic flow segments. The flowrecords of the traffic flow segments at the middleboxes can be generatedby the middleboxes and subsequently exported to the middlebox trafficflow segment collectors 310. More specifically, the flow records can beexported to the middlebox traffic flow segment collectors 310 throughthe IPFIX protocol. The flow records can be exported to the middleboxtraffic flow segment collectors 310 after each of the traffic flowsegments is established, e.g. through the middleboxes. Alternatively,the flow records can be exported to the middlebox traffic flow segmentcollectors 410 after a corresponding traffic flow of the traffic flowsegments is completed, e.g. through the middleboxes.

At step 402, the middlebox traffic flow stitching system 312 identifiessources and destinations of the traffic flow segments in the networkenvironment with respect to the middleboxes using the flow records. Forexample, the middlebox traffic flow stitching system 312 can identifywhether a traffic flow segment is passing from a client to one or themiddleboxes towards a server using the flow records. In another example,the middlebox traffic flow stitching system 312 can identify whether atraffic flow segment is passing from a server to the middleboxes towardsa client using the flow records. In yet another example, the middleboxtraffic flow stitching system 312 can identify whether a traffic flowsegment is passing between the middleboxes, e.g. from the firstmiddlebox to the second middlebox or vice versa. The middlebox trafficflow stitching system 312 can identify flow directions of the trafficflow segments based on either or both sources and destinations of thetraffic flow segments included as part of the flow records. For example,the middlebox traffic flow stitching system 312 can identify a flowdirection of a traffic flow segment based on an IP address of a serverwhere the flow segment started and a SNIP port on one of the first orsecond middleboxes that ends the flow segment at the middlebox.

At step 404, the middlebox traffic flow stitching system 312 stitchestogether the traffic flow segments to form a first stitched traffic flowat the first middlebox and a second stitched traffic flow at the secondmiddlebox. More specifically, the middlebox traffic flow stitchingsystem 312 can stitch together the traffic flow segments to form a firststitched traffic flow at the first middlebox and a second stitchedtraffic flow at the second middlebox based on one or more transactionidentifiers assigned to the traffic flow segments. Further, themiddlebox traffic flow stitching system 312 can stitch together thetraffic flow segments to form a first stitched traffic flow at the firstmiddlebox and a second stitched traffic flow at the second middleboxbased on the sources and destinations of the traffic flow segments withrespect to the middleboxes. For example, the traffic flow segmentssharing the same transaction identifier can be stitched together basedon the directions of the traffic flow segments to form the first andsecond stitched traffic flows, e.g. based on the flow records. Morespecifically, the one or more transaction identifiers assigned to thetraffic flow segments can be indicated by the flow records collected atstep 400 and subsequently used to stitch the traffic flow segmentstogether to form the first and second stitched traffic flows.

At step 406, the middlebox traffic flow stitching system 312 stitchestogether the first stitched traffic flow and the second stitched trafficflow to from a cross-middlebox stitched traffic flow across the firstmiddlebox and the second middlebox. Specifically, the middlebox trafficflow stitching system 312 can stitch together the first stitched trafficflow and the second stitched traffic flow to form the cross-middleboxstitched traffic flow based on the sources and destinations of thetraffic flow segments with respect to the middleboxes. Further, themiddlebox traffic flow stitching system 312 can stitch together thefirst stitched traffic flow and the second stitched traffic flow to formthe cross-middlebox stitched traffic flow based on common traffic flowsegments between the first stitched traffic flow and the second stitchedtraffic flow, e.g. as indicated by the sources and destinations of thetraffic flow segments.

At step 408, the middlebox traffic flow stitching system 312incorporates the cross-middlebox stitched traffic flow as part ofnetwork traffic data for the network environment. Specifically, thecross-middlebox stitched traffic flow can be incorporated as part ofidentified traffic flows in the network environment that are included aspart of the network traffic data for the network environment. Forexample, the cross-middlebox stitched traffic flow can be stitched totraffic flows identified in a network fabric of the network environment,as part of incorporating the stitched traffic flow with network data forthe network environment including the network fabric.

FIG. 5 shows an example middlebox traffic flow stitching system 500. Themiddlebox traffic flow stitching system 500 can function according to anapplicable system for stitching together traffic flow segments through amiddlebox to form a stitched traffic flow and correspondingcross-middlebox stitched traffic flows, such as the middlebox trafficflow stitching system 312 shown in FIG. 3. The middlebox traffic flowstitching system 500 can stitch together traffic flows using flowrecords collected or otherwise exported from a middlebox. Specifically,the middlebox traffic flow stitching system 500 can identify sources anddestinations of traffic flow segments, and potentially correspondingflow directions of the segments, and subsequently stitch togethertraffic flows based on transaction identifiers assigned to the trafficflow segments and the sources and destinations of the traffic flowsegments.

All of portions of the middlebox traffic flow stitching system 500 canbe implemented at an applicable collector for collecting flow recordsfrom a middlebox, such as the middlebox traffic flow segment collectors310 shown in FIG. 3. Additionally, all or portions of the middleboxtraffic flow stitching system 500 can be implemented at a middlebox,e.g. as part of an agent. Further, all or portions of the middleboxtraffic flow stitching system 500 can be implemented at an applicablesystem for monitoring network traffic in a network environment, such asthe network traffic monitoring system 100 shown in FIG. 1.

The middlebox traffic flow stitching system 500 includes a flow recordshash table maintainer 502, a flow records hash table datastore 504, atraffic flow segment stitcher 506, and a completed flow identifier 508.The flow records hash table maintainer 502 functions to maintain a flowrecords hash table. The flow records hash table maintainer 502 canmaintain a hash table based on flow records collected from or otherwiseexported by a middlebox. In maintaining a flow records hash table, theflow records hash table maintainer 502 can generate and update one ormore flow records hash table stored in the flow records hash tabledatastore 504.

TABLE 1 T1 C -> VIP T1 IP -> Server T1 Server -> IP T1 VIP -> C T2C->VIP

Table 1, shown above, illustrates an example of a flow records hashtable maintained by the flow records hash table maintainer 502 andstored in the flow records hash table datastore 504. The example flowrecords hash table includes a plurality of entries. Each entrycorresponds to a traffic flow segment passing through a middlebox.Further, each entry includes a transaction identifier and a source anddestination identifier for each traffic flow segment. For example, thefirst entry corresponds to a traffic flow segment passing from theclient, C, to a port on the middlebox, VIP. Further in the example, thefirst entry includes a transaction identifier, T1, assigned to thetraffic flow segment, e.g. by a middlebox. In another example, thesecond entry corresponds to a second traffic flow segment passing fromthe middlebox, IP, to a server, signified by “Server” in the entry. Flowrecords hash tables can include entries with different transactionidentifiers corresponding to different traffic flows. Specifically, theexample flow records hash table has a first entry including a firsttransaction identifier T1 and a fifth entry including a secondtransaction identifier T2.

The traffic flow segment stitcher 506 functions to stitch togethertraffic flow segments at a middlebox to form a stitched traffic flowcorresponding to a traffic flow through the middlebox. Specifically, thetraffic flow segment stitcher 506 can stitch together traffic flowsegments using a flow records hash table, e.g. stored in the flowrecords hash table datastore 504. In using a flow records hash table tostitch together traffic flow segments, the traffic flow segment stitcher506 can stitch together traffic flows based on transaction identifiersincluded as part of entries corresponding to traffic flow segments inthe flow records hash table. For example, the traffic flow segmentstitcher 506 can stitch together a first traffic flow segmentcorresponding to the first entry in the example hash table and a secondtraffic flow segment corresponding to the second entry in the examplehash table based on both entries including the same transactionidentifier T1.

Further, the traffic flow segment stitcher 506 can function to stitchtogether stitched traffic flows to form a cross-middlebox stitchedtraffic flow. Specifically, the traffic flow segment stitcher 506 canstitch together stitched traffic flows using one or more flow recordshash tables, e.g. stored in the flow records hash table datastore 504.More specifically, the traffic flow segment stitcher 506 can use flowrecords hash tables to identify common traffic flow segments betweenstitched traffic flows. Subsequently, the traffic flow segment stitcher506 can stitch together the stitched traffic flow based on the commontraffic flow segments to form a cross-middlebox stitched traffic flow.

In using a flow records hash table to stitch together traffic flowsegments, the traffic flow segment stitcher 506 can group entries in thehash table to form grouped entries and subsequently use the groupedentries to stitch together traffic flow segments and already stitchedtraffic flow segments. More specifically, the traffic flow segmentstitcher 506 can group entries that share a transaction identifier toform grouped entries. For example, the traffic flow segment stitcher 506can group the first four entries together based on the entries allhaving the same transaction identifier T1. Subsequently, based onentries being grouped together to form grouped entries, the traffic flowsegment stitcher 506 can stitch together traffic flows corresponding tothe entries in the grouped entries. For example, the traffic flowsegment stitcher 506 can group the first four entries in the exampleflow records hash table and subsequently stitch traffic flow segmentscorresponding to the first four entries based on the grouping of thefirst four entries.

Further, in using a flow records hash table to stitch together trafficflow segments and already stitched traffic flow segments, the trafficflow segment stitcher 506 can identify flow directions and/or sourcesand destinations of the traffic flow segments based on correspondingentries of the traffic flow segments in the flow records hash table.More specifically, the traffic flow segment stitcher 506 can identifyflow directions of traffic flow segments based on identifiers of sourcesand destinations of the segments in corresponding entries in a flowrecords hash table. For example, the traffic flow segment stitcher 506can identify that a traffic flow segment corresponding to the firstentry in the example hash table moves from a client to a middlebox basedon the flow segment originating at the client and terminating at a VIPport at the middlebox, as indicated by the first entry in the table.Subsequently, using flow directions and/or sources and destinations oftraffic flow segments identified from a flow records hash table, thetraffic flow segment stitcher 506 can actually stitch together thetraffic flow segments to form either or both stitched traffic flow andcross-middlebox stitched traffic flows. For example, the traffic flowsegment stitcher 506 can stitch a traffic flow segment corresponding tothe fourth entry in the example hash table after a traffic flow segmentcorresponding to the third entry in the example hash table.

The traffic flow segment stitcher 506 can stitch together traffic flowsegments to form stitched traffic flow segments based on both directionsof the traffic flow segments, as identified from corresponding entriesin a flow records hash table, and transaction identifiers included inthe flow records hash table. Specifically, the traffic flow segmentstitcher 506 can identify to stitch together traffic flow segments withcorresponding entries in a flow records hash table that share a commontransaction identifier. For example, the traffic flow segment stitcher506 can determine to stitch together traffic flow segments correspondingto the first four entries in the example flow records hash table basedon the first four entries sharing the same transaction identifier T1.Additionally, the traffic flow segment stitcher 506 can determine anorder to stitch together traffic flow segments based on flow directionsof the traffic flow segments identified from corresponding entries ofthe segments in a flow records hash table. For example, the traffic flowsegment stitcher 506 can determine to stitch together a third trafficflow segment corresponding to the third entry in the example hash tableafter a second traffic flow segment corresponding to the second entry inthe example hash table based on flow directions of the segmentsidentified from the entries.

The completed flow identifier 508 functions to identify a completedtraffic flow occurring through the middlebox. A completed traffic flowcan correspond to establishment of a connection between a client and aserver and vice versa. For example, a completed traffic flow can includea request transmitted from a client to a middlebox, and the requesttransmitted from the middlebox to another middlebox before it is finallytransmitted to a server. Further in the example, the completed trafficflow can include completion of the request from the client to the serverthrough the middleboxes and completion of a response to the request fromthe server to the client through the middleboxes. The completed flowidentifier 508 can identify a completed flow based on flow records. Morespecifically, the completed flow identifier 508 can identify a completedflow based on one or more flow records hash tables. For example, thecompleted flow identifier 508 can identify the first four entries form acompleted flow based on both the first entry beginning at the client andthe last entry ending at the client, and all entries having the sametransaction identifier T1.

The traffic flow segment stitcher 506 can push or otherwise exporttraffic flow data for stitched traffic flows and correspondingcross-middlebox stitched traffic flows. More specifically, the trafficflow segment stitcher 506 can export traffic flow data for incorporationwith network traffic data for a network environment. For example, thetraffic flow segment stitcher 506 can export traffic flow data to thenetwork traffic monitoring system 100, where the traffic flow data canbe combined with network traffic data for a network environment. Thetraffic flow segment stitcher 506 can push traffic flow data based onidentification of a completed traffic flow by the completed flowidentifier 508. More specifically, the traffic flow segment stitcher 506can export traffic flow data indicating a stitched traffic flow of acompleted traffic flow upon identification that the traffic flow isactually a completed flow. This can ensure that data for stitchedtraffic flows and corresponding cross-middlebox stitched traffic flowsis only pushed or otherwise provided when it is known that the stitchedtraffic flows correspond to completed traffic flows.

The disclosure now turns to FIGS. 6 and 7, which illustrate examplenetwork devices and computing devices, such as switches, routers, loadbalancers, client devices, and so forth.

FIG. 6 illustrates an example network device 600 suitable for performingswitching, routing, load balancing, and other networking operations.Network device 600 includes a central processing unit (CPU) 604,interfaces 602, and a bus 610 (e.g., a PCI bus). When acting under thecontrol of appropriate software or firmware, the CPU 604 is responsiblefor executing packet management, error detection, and/or routingfunctions. The CPU 604 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 604 may include one or moreprocessors 608, such as a processor from the INTEL X86 family ofmicroprocessors. In some cases, processor 608 can be specially designedhardware for controlling the operations of network device 600. In somecases, a memory 606 (e.g., non-volatile RAM, ROM, etc.) also forms partof CPU 604. However, there are many different ways in which memory couldbe coupled to the system.

The interfaces 602 are typically provided as modular interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the network device 600. Among theinterfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. In addition, various very high-speed interfaces may beprovided such as fast token ring interfaces, wireless interfaces,Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5Gcellular interfaces, CAN BUS, LoRA, and the like. Generally, theseinterfaces may include ports appropriate for communication with theappropriate media. In some cases, they may also include an independentprocessor and, in some instances, volatile RAM. The independentprocessors may control such communications intensive tasks as packetswitching, media control, signal processing, crypto processing, andmanagement. By providing separate processors for the communicationsintensive tasks, these interfaces allow the master microprocessor 604 toefficiently perform routing computations, network diagnostics, securityfunctions, etc.

Although the system shown in FIG. 6 is one specific network device ofthe present subject matter, it is by no means the only network devicearchitecture on which the present subject matter can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc., is often used.Further, other types of interfaces and media could also be used with thenetwork device 600.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 606) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc. Memory 606could also hold various software containers and virtualized executionenvironments and data.

The network device 600 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform routingand/or switching operations. The ASIC can communicate with othercomponents in the network device 600 via the bus 610, to exchange dataand signals and coordinate various types of operations by the networkdevice 600, such as routing, switching, and/or data storage operations,for example.

FIG. 7 illustrates a computing system architecture 700 wherein thecomponents of the system are in electrical communication with each otherusing a connection 705, such as a bus. Exemplary system 700 includes aprocessing unit (CPU or processor) 710 and a system connection 705 thatcouples various system components including the system memory 715, suchas read only memory (ROM) 720 and random access memory (RAM) 725, to theprocessor 710. The system 700 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 710. The system 700 can copy data from the memory 715and/or the storage device 730 to the cache 712 for quick access by theprocessor 810. In this way, the cache can provide a performance boostthat avoids processor 710 delays while waiting for data. These and othermodules can control or be configured to control the processor 710 toperform various actions. Other system memory 715 may be available foruse as well. The memory 715 can include multiple different types ofmemory with different performance characteristics. The processor 710 caninclude any general purpose processor and a hardware or softwareservice, such as service 1 732, service 2 734, and service 3 736 storedin storage device 730, configured to control the processor 710 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 710 may bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

To enable user interaction with the system 700, an input device 745 canrepresent any number of input mechanisms, such as a microphone forspeech, a touch-sensitive screen for gesture or graphical input,keyboard, mouse, motion input, speech and so forth. An output device 735can also be one or more of a number of output mechanisms known to thoseof skill in the art. In some instances, multimodal systems can enable auser to provide multiple types of input to communicate with the system700. The communications interface 740 can generally govern and managethe user input and system output. There is no restriction on operatingon any particular hardware arrangement and therefore the basic featureshere may easily be substituted for improved hardware or firmwarearrangements as they are developed.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 725, read only memory (ROM) 720, andhybrids thereof.

The storage device 730 can include services 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the systemconnection 705. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, connection 705, output device735, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, medius, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

What is claimed is:
 1. A method comprising: collecting flow records oftraffic flow segments at both a first middlebox and a second middleboxin a network environment corresponding to one or more traffic flowspassing through either or both the first middlebox and the secondmiddlebox, the flow records including one or more transactionidentifiers assigned to the traffic flows; identifying sources anddestinations of the traffic flow segments in the network environmentwith respect to either or both the first middlebox or the secondmiddlebox using the flow records; stitching together a subset of thetraffic flow segments to form a first stitched traffic flow at the firstmiddlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and thesources and destinations of the traffic flow segments in the networkenvironment with respect to the first middlebox; stitching togetheranother subset of the traffic flow segments to form a second stitchedtraffic flow at the second middlebox in the network environment based onthe one or more transaction identifiers assigned to the traffic flowsegments and the sources and destinations of the traffic flow segmentsin the network environment with respect to the second middlebox;stitching together the first stitched traffic flow and the secondstitched traffic flow to form a cross-middlebox stitched traffic flowacross the first middlebox and the second middlebox; and incorporatingthe cross-middlebox stitched traffic flow as part of network trafficdata for the network environment.
 2. The method of claim 1, wherein thesubset of the traffic flow segments and the another subset of thetraffic flow segments share common traffic flow segments of the trafficflow segments.
 3. The method of claim 2, wherein the first stitchedtraffic flow and the second stitched traffic flow are stitched togetherto identify the cross-middlebox stitched traffic flow according to thecommon traffic flow segments of the traffic flow segments.
 4. The methodof claim 1, wherein the first stitched traffic flow and the secondstitched traffic flow are stitched together to identify thecross-middlebox stitched traffic flow based on the sources and thedestinations of the traffic flow segments in the subset of the trafficflow segments and the another subset of the traffic flow segments. 5.The method of claim 1, wherein the cross-middlebox stitched traffic flowforms a complete flow of the one or more traffic flows for a transactionbetween a client and a server in the network environment.
 6. The methodof claim 5, wherein the complete flow of the transaction between theclient and the server include a request originating at the client and aresponse to the request originating at the server.
 7. The method ofclaim 6, wherein the complete flow of the transaction between the clientand the server includes a request sent from the client to the firstmiddlebox and included as part of the traffic flow segments, the requestsent from the first middlebox to the second middlebox and included aspart of the traffic flow segments, the request sent from the secondmiddlebox to the server and included as part of the traffic flowsegments, the response to the request sent from the server to the secondmiddlebox and included as part of the traffic flow segments, theresponse to the request sent from the second middlebox to the firstmiddlebox and included as part of the traffic flow segments, and theresponse to the request sent from the first middlebox to the client andincluded as part of the traffic flow segments.
 8. The method of claim 6,further comprising generating the network traffic data to indicate thatthe complete flow of the transaction between the client and the serverpasses between the server and the client through multiple middleboxes.9. The method of claim 1, wherein the flow records are collected fromthe first middlebox and the second middlebox as the first middlebox andthe second middlebox export the flow records using an Internet ProtocolFlow Information Export protocol.
 10. The method of claim 1, furthercomprising: maintaining a first hash table of the traffic flow segmentsat the first middlebox and a second hash table of the traffic flowsegments at the second middlebox, the first and second hash tables eachincluding an entry corresponding to each traffic flow segment of thetraffic flow segments at either or both the first middlebox and thesecond middlebox, each entry including a source and a destination ofdata in a corresponding traffic flow segment of the entry and atransaction identification associated with the corresponding trafficflow segment; and using the first hash table and the second hash tableof the traffic flow segments to form the cross-middlebox stitchedtraffic flow across the first middlebox and the second middlebox in thenetwork environment based on the one or more transaction identifiersassigned to the traffic flow segments and the sources and destinations,as indicated by entries in the hash table.
 11. The method of claim 10,further comprising: grouping entries of the hash table based on thetraffic flow segments in the entries and the one or more transactionidentifications associated with the traffic flow segments in the entriesto form grouped entries of the hash table; and forming thecross-middlebox stitched traffic flow based on the grouped entries ofthe hash table.
 12. The method of claim 1, further comprising:identifying flow directions of the traffic flow segments in the networkenvironment with respect to either or both the first middlebox and thesecond middlebox using the sources and destinations of the traffic flowsegments in the network environment; and stitching together one or acombination of the first stitched traffic flow, the second stitchedtraffic flow, and the cross-middlebox stitched traffic flow using theflow directions of the traffic flow segments in the network environment.13. The method of claim 1, wherein the cross-middlebox stitched trafficflow is used to create an application dependency mapping as part of thenetwork traffic data for the network environment.
 14. The method ofclaim 1, wherein the cross-middlebox stitched traffic flow is used tocreate a policy for either or both the first middlebox and the secondmiddlebox.
 15. A system comprising: one or more processors; and at leastone computer-readable storage medium having stored therein instructionswhich, when executed by the one or more processors, cause the one ormore processors to perform operations comprising: collecting flowrecords of traffic flow segments at both a first middlebox and a secondmiddlebox in a network environment corresponding to one or more trafficflows passing through either or both the first middlebox and the secondmiddlebox, the flow records including one or more transactionidentifiers assigned to the traffic flows; identifying sources anddestinations of the traffic flow segments in the network environmentwith respect to either or both the first middlebox or the secondmiddlebox using the flow records; stitching together a subset of thetraffic flow segments to form a first stitched traffic flow at the firstmiddlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and thesources and destinations of the traffic flow segments in the networkenvironment with respect to the first middlebox; stitching togetheranother subset of the traffic flow segments to form a second stitchedtraffic flow at the second middlebox in the network environment based onthe one or more transaction identifiers assigned to the traffic flowsegments and the sources and destinations of the traffic flow segmentsin the network environment with respect to the second middlebox; andstitching together the first stitched traffic flow and the secondstitched traffic flow to form a cross-middlebox stitched traffic flowacross the first middlebox and the second middlebox.
 16. The system ofclaim 15, the subset of the traffic flow segments and the another subsetof the traffic flow segments share common traffic flow segments of thetraffic flow segments and the first stitched traffic flow and the secondstitched traffic flow are stitched together to identify thecross-middlebox stitched traffic flow according to the common trafficflow segments of the traffic flow segments.
 17. The system of claim 15,wherein the first stitched traffic flow and the second stitched trafficflow are stitched together to identify the cross-middlebox stitchedtraffic flow based on the sources and the destinations of the trafficflow segments in the subset of the traffic flow segments and the anothersubset of the traffic flow segments.
 18. The system of claim 15, whereinthe instructions which, when executed by the one or more processors,further cause the one or more processors to perform operationscomprising: maintaining a first hash table of the traffic flow segmentsat the first middlebox and a second hash table of the traffic flowsegments at the second middlebox, the first and second hash tables eachincluding an entry corresponding to each traffic flow segment of thetraffic flow segments at either or both the first middlebox and thesecond middlebox, each entry including a source and a destination ofdata in a corresponding traffic flow segment of the entry and atransaction identification associated with the corresponding trafficflow segment; and using the first hash table and the second hash tableof the traffic flow segments to form the cross-middlebox stitchedtraffic flow across the first middlebox and the second middlebox in thenetwork environment based on the one or more transaction identifiersassigned to the traffic flow segments and the sources and destinations,as indicated by entries in the hash table.
 19. The system of claim 15,wherein the flow records are collected from the first middlebox and thesecond middlebox as the first middlebox and the second middlebox exportthe flow records using an Internet Protocol Flow Information Exportprotocol.
 20. A non-transitory computer-readable storage medium havingstored therein instructions which, when executed by a processor, causethe processor to perform operations comprising: collecting flow recordsof traffic flow segments at both a first middlebox and a secondmiddlebox in a network environment corresponding to one or more trafficflows passing through either or both the first middlebox and the secondmiddlebox, the flow records including one or more transactionidentifiers assigned to the traffic flows; identifying sources anddestinations of the traffic flow segments in the network environmentwith respect to either or both the first middlebox or the secondmiddlebox using the flow records; stitching together a subset of thetraffic flow segments to form a first stitched traffic flow at the firstmiddlebox in the network environment based on the one or moretransaction identifiers assigned to the traffic flow segments and thesources and destinations of the traffic flow segments in the networkenvironment with respect to the first middlebox; stitching togetheranother subset of the traffic flow segments to form a second stitchedtraffic flow at the second middlebox in the network environment based onthe one or more transaction identifiers assigned to the traffic flowsegments and the sources and destinations of the traffic flow segmentsin the network environment with respect to the second middlebox, whereinthe subset of the traffic flow segment and the another subset of thetraffic flow segments include common traffic flow segments; stitchingtogether the first stitched traffic flow and the second stitched trafficflow to form a cross-middlebox stitched traffic flow across the firstmiddlebox and the second middlebox; and incorporating thecross-middlebox stitched traffic flow as part of network traffic datafor the network environment.